mirror of
https://github.com/astral-sh/uv
synced 2026-01-24 15:00:18 -05:00
8dbf43c85d
At a high level, this PR adds a smattering of new tests that effectively snapshot the output of `uv lock` for a selection of "ecosystem" projects. That is, real Python projects for which we expect `uv` to work well with. The main idea with these tests is to get a better idea of how changes in `uv` impact the lock files of real world projects. For example, we're hoping that these tests will help give us data for how #5733 differs from #5887. This has already revealed some bugs. Namely, re-running `uv lock` for a second time will produce a different lock file for some projects. So to prioritize getting the tests added, for those projects, we don't do the deterministic checking.
351 lines
8.6 KiB
TOML
351 lines
8.6 KiB
TOML
[project]
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name = "transformers"
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version = "4.39.0.dev0"
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author = "The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)"
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author_email = "transformers@huggingface.co"
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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keywords = ["NLP", "vision", "speech", "deep", "learning", "transformer", "pytorch", "tensorflow", "jax", "BERT", "GPT-2", "Wav2Vec2", "ViT"]
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requires-python = ">=3.9.0"
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classifiers = [
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"Development Status :: 5 - Production/Stable",
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"Intended Audience :: Developers",
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"Intended Audience :: Education",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: Apache Software License",
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"Operating System :: OS Independent",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.8",
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"Programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.10",
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"Topic :: Scientific/Engineering :: Artificial Intelligence"
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]
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dependencies = [
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"filelock",
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"huggingface-hub>=0.19.3,<1.0",
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"numpy>=1.17",
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"packaging>=20.0",
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"pyyaml>=5.1",
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"regex!=2019.12.17",
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"requests",
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"tokenizers>=0.14,<0.19",
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"safetensors>=0.4.1",
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"tqdm>=4.27",
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]
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[project.urls]
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repository = "https://github.com/huggingface/transformers"
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[project.optional-dependencies]
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ja = [
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"fugashi>=1.0",
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"ipadic>=1.0.0,<2.0",
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"unidic_lite>=1.0.7",
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"unidic>=1.0.2",
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"sudachipy>=0.6.6",
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"sudachidict_core>=20220729",
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"rhoknp>=1.1.0,<1.3.1"
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]
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sklearn = ["scikit-learn"]
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tf = [
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"tensorflow>=2.6,<2.16",
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"onnxconverter-common",
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"tf2onnx",
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"tensorflow-text<2.16",
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"keras-nlp>=0.3.1"
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]
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tf-cpu = [
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"tensorflow-cpu>=2.6,<2.16",
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"onnxconverter-common",
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"tf2onnx",
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"tensorflow-text<2.16",
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"keras-nlp>=0.3.1"
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]
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torch = ["torch", "accelerate>=0.21.0"]
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accelerate = ["accelerate>=0.21.0"]
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retrieval = ["faiss-cpu", "datasets!=2.5.0"]
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flax = [
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"jax>=0.4.1,<=0.4.13",
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"jaxlib>=0.4.1,<=0.4.13",
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"flax>=0.4.1,<=0.7.0",
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"optax>=0.0.8,<=0.1.4"
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]
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tokenizers = ["tokenizers>=0.14,<0.19"]
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ftfy = ["ftfy"]
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onnxruntime = ["onnxruntime>=1.4.0", "onnxruntime-tools>=1.4.2"]
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onnx = [
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"onnxconverter-common",
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"tf2onnx",
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"onnxruntime>=1.4.0",
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"onnxruntime-tools>=1.4.2"
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]
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modelcreation = ["cookiecutter==1.7.3"]
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sagemaker = ["sagemaker==2.226.1"] # sagemaker>=2.31.0 # TODO(konsti)
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deepspeed = ["deepspeed>=0.9.3", "accelerate>=0.21.0"]
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optuna = ["optuna"]
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ray = ["ray[tune]>=2.7.0"]
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sigopt = ["sigopt"]
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integrations = ["optuna", "ray[tune]>=2.7.0", "sigopt"]
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serving = ["pydantic", "uvicorn", "fastapi", "starlette"]
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audio = ["librosa", "pyctcdecode>=0.4.0", "phonemizer", "kenlm"]
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speech = [
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"torchaudio",
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"librosa",
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"pyctcdecode>=0.4.0",
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"phonemizer",
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"kenlm"
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]
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torch-speech = [
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"torchaudio",
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"librosa",
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"pyctcdecode>=0.4.0",
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"phonemizer",
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"kenlm"
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]
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tf-speech = ["librosa", "pyctcdecode>=0.4.0", "phonemizer", "kenlm"]
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flax-speech = ["librosa", "pyctcdecode>=0.4.0", "phonemizer", "kenlm"]
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vision = ["Pillow>=10.0.1,<=15.0"]
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timm = ["timm"]
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torch-vision = ["torchvision", "Pillow>=10.0.1,<=15.0"]
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# natten = ["natten>=0.14.6,<0.15.0"] # TODO(konsti)
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codecarbon = ["codecarbon==1.2.0"]
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video = ["decord==0.6.0", "av==9.2.0"]
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sentencepiece = ["sentencepiece>=0.1.91,!=0.1.92", "protobuf"]
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deepspeed-testing = [
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"deepspeed>=0.9.3",
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"accelerate>=0.21.0",
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"pytest>=7.2.0,<8.0.0",
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"pytest-xdist",
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"timeout-decorator",
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"parameterized",
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"psutil",
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"datasets!=2.5.0",
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"dill<0.3.5",
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"evaluate>=0.2.0",
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"pytest-timeout",
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"ruff==0.1.5",
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"sacrebleu>=1.4.12,<2.0.0",
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"rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
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"nltk",
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"GitPython<3.1.19",
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"hf-doc-builder>=0.3.0",
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"protobuf",
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"sacremoses",
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"rjieba",
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"beautifulsoup4",
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"tensorboard",
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"pydantic",
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"faiss-cpu",
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"datasets!=2.5.0",
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"cookiecutter==1.7.3",
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"optuna",
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"sentencepiece>=0.1.91,!=0.1.92",
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"protobuf"
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]
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quality = [
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"datasets!=2.5.0",
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"isort>=5.5.4",
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"ruff==0.1.5",
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"GitPython<3.1.19",
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"hf-doc-builder>=0.3.0",
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"urllib3<2.0.0"
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]
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all = [
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"tensorflow>=2.6,<2.16",
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"onnxconverter-common",
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"tf2onnx",
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"tensorflow-text<2.16",
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"keras-nlp>=0.3.1",
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"torch",
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"accelerate>=0.21.0",
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"jax>=0.4.1,<=0.4.13",
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"jaxlib>=0.4.1,<=0.4.13",
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"flax>=0.4.1,<=0.7.0",
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"optax>=0.0.8,<=0.1.4",
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"sentencepiece>=0.1.91,!=0.1.92",
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"protobuf",
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"tokenizers>=0.14,<0.19",
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"torchaudio",
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"librosa",
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"pyctcdecode>=0.4.0",
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"phonemizer",
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"kenlm",
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"Pillow>=10.0.1,<=15.0",
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"optuna",
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"ray[tune]>=2.7.0",
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"sigopt",
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"timm",
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"torchvision",
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"Pillow>=10.0.1,<=15.0",
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"codecarbon==1.2.0",
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"accelerate>=0.21.0",
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"decord==0.6.0",
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"av==9.2.0"
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]
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docs_specific = ["hf-doc-builder"]
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docs = [
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"tensorflow>=2.6,<2.16",
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"onnxconverter-common",
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"tf2onnx",
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"tensorflow-text<2.16",
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"keras-nlp>=0.3.1",
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"torch",
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"accelerate>=0.21.0",
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"jax>=0.4.1,<=0.4.13",
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"jaxlib>=0.4.1,<=0.4.13",
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"flax>=0.4.1,<=0.7.0",
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"optax>=0.0.8,<=0.1.4",
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"sentencepiece>=0.1.91,!=0.1.92",
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"protobuf",
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"tokenizers>=0.14,<0.19",
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"torchaudio",
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"librosa",
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"pyctcdecode>=0.4.0",
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"phonemizer",
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"kenlm",
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"Pillow>=10.0.1,<=15.0",
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"optuna",
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"ray[tune]>=2.7.0",
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"sigopt",
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"timm",
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"torchvision",
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"Pillow>=10.0.1,<=15.0",
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"codecarbon==1.2.0",
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"accelerate>=0.21.0",
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"decord==0.6.0",
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"av==9.2.0",
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"hf-doc-builder"
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]
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torchhub = [
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"filelock",
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"huggingface-hub>=0.19.3,<1.0",
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"importlib_metadata",
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"numpy>=1.17",
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"packaging>=20.0",
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"protobuf",
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"regex!=2019.12.17",
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"requests",
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"sentencepiece>=0.1.91,!=0.1.92",
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"torch",
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"tokenizers>=0.14,<0.19",
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"tqdm>=4.27"
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]
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agents = [
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"diffusers",
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"accelerate>=0.21.0",
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"datasets!=2.5.0",
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"torch",
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"sentencepiece>=0.1.91,!=0.1.92",
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"opencv-python",
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"Pillow>=10.0.1,<=15.0"
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]
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dev-dependencies = [
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"GitPython<3.1.19",
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"Pillow>=10.0.1,<=15.0",
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"accelerate>=0.21.0",
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"av==9.2.0",
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"beautifulsoup4",
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"codecarbon==1.2.0",
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"codecarbon==1.2.0",
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"cookiecutter==1.7.3",
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"datasets!=2.5.0",
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"decord==0.6.0",
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"dill<0.3.5",
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"evaluate>=0.2.0",
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"faiss-cpu",
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"flax>=0.4.1,<=0.7.0",
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"fugashi>=1.0",
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"hf-doc-builder",
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"hf-doc-builder>=0.3.0",
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"ipadic>=1.0.0,<2.0",
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"isort>=5.5.4",
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"jax>=0.4.1,<=0.4.13",
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"jaxlib>=0.4.1,<=0.4.13",
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"kenlm",
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"keras-nlp>=0.3.1",
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"librosa",
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"nltk",
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"onnxconverter-common",
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"onnxruntime-tools>=1.4.2",
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"onnxruntime>=1.4.0",
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"optax>=0.0.8,<=0.1.4",
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"optuna",
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"parameterized",
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"phonemizer",
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"protobuf",
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"psutil",
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"pyctcdecode>=0.4.0",
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"pydantic",
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"pytest-timeout",
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"pytest-xdist",
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"pytest>=7.2.0,<8.0.0",
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"ray[tune]>=2.7.0",
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"rhoknp>=1.1.0,<1.3.1",
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"rjieba",
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"rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
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"ruff==0.1.5",
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"sacrebleu>=1.4.12,<2.0.0",
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"sacremoses",
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"scikit-learn",
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"sentencepiece>=0.1.91,!=0.1.92",
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"sigopt",
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"sudachidict_core>=20220729",
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"sudachipy>=0.6.6",
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"tensorboard",
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"tensorflow-text<2.16",
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"tensorflow>=2.6,<2.16",
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"tf2onnx",
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"timeout-decorator",
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"timm",
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"tokenizers>=0.14,<0.19",
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"torch",
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"torchaudio",
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"torchvision",
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"unidic>=1.0.2",
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"unidic_lite>=1.0.7",
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"urllib3<2.0.0"
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]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.ruff]
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# Never enforce `E501` (line length violations).
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ignore = ["C901", "E501", "E741", "F402", "F823"]
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select = ["C", "E", "F", "I", "W"]
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line-length = 119
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# Ignore import violations in all `__init__.py` files.
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[tool.ruff.per-file-ignores]
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"__init__.py" = ["E402", "F401", "F403", "F811"]
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"src/transformers/file_utils.py" = ["F401"]
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"src/transformers/utils/dummy_*.py" = ["F401"]
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[tool.ruff.isort]
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lines-after-imports = 2
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known-first-party = ["transformers"]
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[tool.ruff.format]
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# Like Black, use double quotes for strings.
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quote-style = "double"
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# Like Black, indent with spaces, rather than tabs.
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indent-style = "space"
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# Like Black, respect magic trailing commas.
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skip-magic-trailing-comma = false
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# Like Black, automatically detect the appropriate line ending.
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line-ending = "auto"
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[tool.pytest.ini_options]
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doctest_optionflags = "NUMBER NORMALIZE_WHITESPACE ELLIPSIS"
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doctest_glob = "**/*.md"
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markers = [
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"flash_attn_test: marks tests related to flash attention (deselect with '-m \"not flash_attn_test\"')",
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"bitsandbytes: select (or deselect with `not`) bitsandbytes integration tests",
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]
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